Parameterization of SWIM Mobility Model Using Contact Traces
Zeynep Vatandas, Manikandan Venkateswaran, Koojana Kuladinithi,, Andreas Timm-Giel

TL;DR
This paper presents a method to tune the SWIM mobility model parameters using real contact traces to better simulate human movement patterns in opportunistic networks.
Contribution
It introduces a parameterization approach for the SWIM model based on real contact trace data, improving its realism for OppNets simulations.
Findings
SWIM model parameters can be effectively tuned using real contact traces.
Tuned SWIM model closely matches real contact durations and inter-contact times.
Enhanced mobility modeling improves OppNets data dissemination evaluation.
Abstract
Opportunistic networks (OppNets) are focused to exploit direct, localised communications which occur in a peer-to-peer manner mostly based on people's movements and their contact durations. Therefore, the use of realistic mobility models is critical to evaluate the data dissemination in OppNets. One of the mobility models that is available in OMNeT++ which can be used to mimic human movement patterns is Small Worlds in Motion (SWIM). The SWIM model is based on the intuition that humans often visit nearby locations and if the visited location is far away, then it is probably due to the popularity of the location. As an alternative to mobility of a node, pairwise contact probabilities are also used to evaluate the data dissemination in OppNets. Pairwise contact probabilities can be used to predict that a node will be met by a particular node. These probabilities can be derived in many…
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Taxonomy
TopicsOpportunistic and Delay-Tolerant Networks · Mobile Ad Hoc Networks · Human Mobility and Location-Based Analysis
